def main():
# Generate recording object
reco_cam = Record.Camera(saving_directory="./")
video_name = "test_record.mp4"
reco_time = 10
# Start recording:
print("Starting recording for {} sec.".format(reco_time))
reco_cam.record(video_name)
time.sleep(reco_time)
# Stop recording
reco_cam.stop()
time.sleep(2)
print("Recording finished.")
# Check if video is saved:
files = os.listdir('.')
if video_name in files:
print("{} found !".format(video_name))
else:
print("video not saved: {}".format(files))
def main():
## Get camera for recording
reco_cam = Record.Camera()
reco_led = Record.LED()
vid_id = 0
recording = False
stop_count = 0
## Model file
parser = argparse.ArgumentParser()
parser.add_argument('-m',
'--model',
help="Model file (.tflite)",
dest='model_file',
required=True)
args = parser.parse_args()
print("Loading {}.".format(args.model_file))
interpreter = tflite.Interpreter(model_path=args.model_file)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
input_shape = input_details[0]['shape']
output_details = interpreter.get_output_details()
## Get Audio Stream
audio_interface = pyaudio.PyAudio()
usb_audio_i = find_index_usb_audio(audio_interface)
form_1 = pyaudio.paInt16 #16-bit resolution
chans = 1 # 1 channel
sample_rate = 22050 # 44.1kHz sampling rate
chunk = 1 * sample_rate # 2^12 samples for buffer
print("Audio parameters:")
print(" {} | {} | {} | {}".format(form_1, chans, sample_rate, chunk))
audio_stream = audio_interface.open(format=form_1,
rate=sample_rate,
channels=chans,
input_device_index=usb_audio_i,
input=True,
frames_per_buffer=chunk)
print("\nAudio ready.")
# Main Loop
audio_stream.start_stream()
try:
while True:
data = np.fromstring(audio_stream.read(chunk), dtype=np.int16)
# Pause the audio stream
audio_stream.stop_stream()
print("\nData: {}".format(data.shape))
start = time.time()
# Extract feature:
feature = extract_feature(data)
## TODO add feature.shape == input_shape check as test.
interpreter.set_tensor(input_details[0]['index'], feature)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
elapsed_time = time.time() - start
print("Feature ~ Inference in {} ms.".format(elapsed_time * 1000))
## Post processing:
current_state = DRONE_STATE[np.argmax(output_data)]
print("Current STATE: {}".format(current_state))
## Recording simple logic:
if current_state == "ARMED" and not recording:
vid_name = "vid_test_" + str(vid_id) + ".mp4"
reco_cam.record(vid_name)
reco_led.blink()
recording = True
stop_count = 0
if current_state == "DISARMED" and recording:
if stop_count == 2:
reco_cam.stop()
reco_led.stop()
recording = False
vid_id += 1
else:
stop_count += 1
# Resume the audio stream
audio_stream.start_stream()
except KeyboardInterrupt:
print('Requested to terminate')
finally:
audio_stream.stop_stream()
audio_stream.close()
audio_interface.terminate()
print('Terminated')